An evaluation method of rock pore volume compressibility determination using a computed tomography scanned based finite element model

Moosavi, Seyed Amin; Goshtasbi, Kamran; Kazemzadeh, Ezzatollah

doi:10.1007/s11600-022-00874-9

Artykuł - szczegóły

Tytuł artykułu

An evaluation method of rock pore volume compressibility determination using a computed tomography scanned based finite element model

Autorzy

Moosavi Seyed Amin , Goshtasbi Kamran , Kazemzadeh Ezzatollah

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-022-00874-9

Warianty tytułu

Języki publikacji

Abstrakty

This paper has proposed a new perspective of studying internal structure-based tests, the results of which will improve the present experimental methods and enrich our understanding of rock structure-based modeling without any core preparations, with low cost in a short time. Pore volume compressibility (PVC) is an important feature of rock and is related to mechanical and structural behavior of porous rock sample. An accurate evaluation of pore volume compressibility depends on experimental test which is time-consuming and costly. This paper outlines new method for evaluation of PVC of rock cores using a computed tomography (CT) scan-based finite element method (FEM). The verification studies were performed on a series of porous rock cores which were extracted from deep oil reservoirs in Iran. In order to construct a finite element model, a relationship between spatial elastic properties of samples and CT-scanned data images was derived. The samples were scanned by a conic beam computed tomography (CBCT) machine, and the scanned data were converted into a model to simulate PVC tests. The pore volumetric strains were obtained from a linear elastic analysis for each stress and pore pressure step. To validate the finite element analysis (FEA) results, a series of experimental PVC tests were conducted on the pre-scanned samples and PVC curves were extracted. As a result, the predictions calculated from the CT scan-based numerical models have shown a good correlation with the results obtained from laboratory experiments. The results revealed that it is possible to simulate PVC tests using this numerical proposed evaluation method in such a way that the cost and time of the tests were lowered.

Słowa kluczowe

rock computed tomography pore volume compressibility digital core laboratory

skała tomografia komputerowa

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2023

Tom

Vol. 71, no. 1

Strony

147--159

Opis fizyczny

Bibliogr. 56 poz.

Twórcy

autor

Moosavi Seyed Amin

aminmoosavi@vru.ac.ir

Mining Engineering Department, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

https://orcid.org/0000-0003-4196-0158

autor

Goshtasbi Kamran

Department of Mining Engineering, Tarbiat Modares University, Tehran, Iran

autor

Kazemzadeh Ezzatollah

Division of Petroleum Engineering, Faculty of Upstream Petroleum Industry, Research Institute of Petroleum Industry (RIPI), Tehran, Iran

Bibliografia

1. Aloki Bakhtiari HAV, Moosavi A, Kazemzadeh E, Goshtasbi K, Esfahani MR, Vali J (2011) The effect of rock types on pore volume compressibility of limestone and dolomite samples. Geopersia 1(1):37–82. https://doi.org/10.22059/jgeope.2011.22163
2. Al-Raoush RI, Willson CS (2005) Extraction of physically realistic pore network properties from three-dimensional synchrotron X-ray microtomography images of unconsolidated porous media systems. J Hydrol 300(1):44–64. https://doi.org/10.1016/j.jhydrol.2004.05.005
3. Basu D, Kumar SS (1995) Importing mesh entities through IGES/PDES. Adv Eng Softw 23(3):151–161. https://doi.org/10.1016/0965-9978(95)00075-5
4. Bauer D, Youssef S, Han M, Bekri S, Rosenberg E, Fleury M, Vizika O (2011) From computed microtomography images to resistivity index calculations of heterogeneous carbonates using a dual-porosity pore-network approach: Influence of percolation on the electrical transport properties. Phys Rev E 84(1):011133. https://doi.org/10.1103/PhysRevE.84.011133
5. Christe P, Turberg P, Labiouse V, Meuli R, Parriaux A (2011) An X-ray computed tomography-based index to characterize the quality of cataclastic carbonate rock samples. Eng Geol 117(3–4):180–188. https://doi.org/10.1016/j.enggeo.2010.10.016
6. Cnudde V, Masschaele B, Dierick M, Vlassenbroeck J, Hoorebeke LV, Jacobs P (2006) Recent progress in X-ray CT as a geosciences tool. Appl Geochem 21(5):826–832. https://doi.org/10.1016/j.apgeochem.2006.02.010
7. Das V, Saxena N, Hofmann R (2020) Compressibility predictions using digital thin-section images of rocks. Comput Geosci 139:104482. https://doi.org/10.1016/j.cageo.2020.104482
8. Deng H, Stauffer PH, Dai Z, Jiao Z, Surdam RC (2012) Simulation of industrial-scale CO2 storage: Multi-scale heterogeneity and its impacts on storage capacity, injectivity and leakage. Int J Greenhouse Gas Control 10:397–418. https://doi.org/10.1016/j.ijggc.2012.07.003
9. Feng X-T, Chen S, Zhou H (2004) Real-time computerized tomography (CT) experiments on sandstone damage evolution during triaxial compression with chemical corrosion. Int J Rock Mech Min Sci 41(2):181–192. https://doi.org/10.1016/S1365-1609(03)00059-5
10. Fotina I, Hopfgartner J, Stock M, Steininger T, Lütgendorf-Caucig C, Georg D (2012) Feasibility of CBCT-based dose calculation: Comparative analysis of HU adjustment techniques. Radiother Oncol 104(2):249–256. https://doi.org/10.1016/j.radonc.2012.06.007
11. Gonçalves OD, Boldt S, Nadaes M, Devito KL (2018) Evaluating the scattered radiation intensity in CBCT. Radiat Phys Chem 144:159–164. https://doi.org/10.1016/j.radphyschem.2017.07.019
12. Hall HN (1953) Compressibility of Reservoir Rocks. Petroleum Transactions, AIME 198:309–311. https://doi.org/10.2118/953309-G
13. Hambli R (2013) Micro-CT finite element model and experimental validation of trabecular bone damage and fracture. Bone 56(2):363–374. https://doi.org/10.1016/j.bone.2013.06.028
14. Harari Z, Shu-Teh W, Salih S (1995) Pore-compressibility study of Arabian carbonate reservoir rocks. SPE Form Eval 10(04):207–214. https://doi.org/10.2118/27625-PA
15. Helgason B, Perilli E, Schileo E, Taddei F, Brynjólfsson S, Viceconti M (2008) Mathematical relationships between bone density and mechanical properties: a literature review. Clin Biomech 23(2):135–146. https://doi.org/10.1016/j.clinbiomech.2007.08.024
16. Huang L, Baud P, Cordonnier B, Renard F, Liu L, Wong T-F (2019) Synchrotron X-ray imaging in 4D: multiscale failure and compaction localization in triaxially compressed porous limestone. Earth Planet Sci Lett 528:115831. https://doi.org/10.1016/j.epsl.2019.115831
17. IGES/PDES. (2001). The Initial Graphics Exchange Specification (IGES) Version 6.0. In
18. Jalalh AA (2006) Compressibility of porous rocks: part I. measurements of hungarian reservoir rock samples. Acta Geophysica 54(3):319–332
19. Jalalh AA (2006b) Compressibility of porous rocks: part II. New Relationsh Acta Geophysica 54(4):399–412. https://doi.org/10.2478/s11600-006-0029-4
20. Josh M, Esteban L, Delle Piane C, Sarout J, Dewhurst DN, Clennell MB (2012) Laboratory characterisation of shale properties. J Petrol Sci Eng 88:107–124. https://doi.org/10.1016/j.petrol.2012.01.023
21. Karpyn ZT, Alajmi A, Radaelli F, Halleck PM, Grader AS (2009) X-ray CT and hydraulic evidence for a relationship between fracture conductivity and adjacent matrix porosity. Eng Geol 103(3):139–145. https://doi.org/10.1016/j.enggeo.2008.06.017
22. Katsumata A, Hirukawa A, Okumura S, Naitoh M, Fujishita M, Ariji E, Langlais RP (2009) Relationship between density variability and imaging volume size in cone-beam computerized tomographic scanning of the maxillofacial region: an in vitro study. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology 107(3):420–425. https://doi.org/10.1016/j.tripleo.2008.05.049
23. Khajeh MM, Chalaturnyk RJ, Boisvert J (2011). Impact of heterogeneous geomechanical properties on coupled geomechanical-flow simulation of sagd. Paper presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, Abu Dhabi, UAE
24. Larmagnat S, Des Roches M, Daigle L-F, Francus P, Lavoie D, Raymond J, Aubiès-Trouilh A (2019) Continuous porosity characterization: metric-scale intervals in heterogeneous sedimentary rocks using medical CT-scanner. Mar Pet Geol 109:361–380. https://doi.org/10.1016/j.marpetgeo.2019.04.039
25. Liu, H.-H., Rutqvist, J., & Berryman, J. G. (2009). On the relationship between stress and elastic strain for porous and fractured rock. Int J Rock Mech Mining Sci, 46(2):289–296. Retrieved from http://www.sciencedirect.com/science/article/B6V4W-4SK0C66-4/2/5b41418b693c3aa7854b1b11de4f374a
26. Lopes A, Brodlie K (2003) Improving the robustness and accuracy of the marching cubes algorithm for isosurfacing. IEEE Trans Visual Comput Gr 9(1):16–29
27. Ma T, Yang C, Chen P, Wang X, Guo Y (2016) On the damage constitutive model for hydrated shale using CT scanning technology. J Nat Gas Sci Eng 28:204–214. https://doi.org/10.1016/j.jngse.2015.11.025
28. Mah P, Reeves TE, McDavid WD (2010) Deriving Hounsfield units using grey levels in cone beam computed tomography. Dentomaxillofacial Radiol 39(6):323–335. https://doi.org/10.1259/dmfr/19603304
29. Needham AW, Abel RL, Tomkinson T, Grady MM (2013) Martian subsurface fluid pathways and 3D mineralogy of the Nakhla meteorite. Geochimica et Cosmochimica Acta 116:96–110
30. Nixon MS, Aguado AS (2013) Feature extraction & image processing for computer vision, Third Edition. Academic Press
31. Razi T, Niknami M, Alavi Ghazani F (2014) Relationship between Hounsfield Unit in CT Scan and Gray Scale in CBCT. Journal of Dental Research, Dental Clinics, Dental Prospects 8(2):107–110. https://doi.org/10.5681/joddd.2014.019
32. Renard F, Bernard D, Desrues J, Ougier-Simonin A (2009) 3D imaging of fracture propagation using synchrotron X-ray microtomography. Earth Planet Sci Lett 286(1):285–291. https://doi.org/10.1016/j.epsl.2009.06.040
33. Rutqvist J, Tsang C-F (2003) Analysis of thermal–hydrologic–mechanical behavior near an emplacement drift at Yucca Mountain. J Contam Hydrol 62:637–652. https://doi.org/10.1016/S0169-7722(02)00184-5
34. Sato A, Obara Y (2017) Analysis of pore structure and water permeation property of a shale rock by means of X-ray CT. Proc Eng 191:666–673. https://doi.org/10.1016/j.proeng.2017.05.230
35. Saxena N, Hows A, Hofmann R, Alpak FO, Dietderich J, Appel M, De Jong H (2019) Rock properties from micro-CT images: digital rock transforms for resolution, pore volume, and field of view. Adv Water Resour 134:103419. https://doi.org/10.1016/j.advwatres.2019.103419
36. Scarfe WC, Farman AG (2008) What is cone-beam CT and how does it work? Dent Clin North Am 52(4):707–730. https://doi.org/10.1016/j.cden.2008.05.005
37. Schoonmaker SJ (2002) The CAD guidebook: a basic manual for understanding and improving computer-aided design, 1st edn. CRC Press
38. Shan P, Lai X (2019) Influence of CT scanning parameters on rock and soil images. J vis Commun Image Represent 58:642–650. https://doi.org/10.1016/j.jvcir.2018.12.014
39. Siddiqui S, Funk JJ, Khamees AA, Al-Harbi AM (2008) Recent advances in the measurement of pore volume compressibility of reservoir rocks. International Symposium of the Society of Core Analysts, Abu Dhabi, 29 October–2 November
40. Suekane T, Thanh NH, Matsumoto T, Matsuda M, Kiyota M, Ousaka A (2009) Direct measurement of trapped gas bubbles by capillarity on the pore scale. Energy Proc 1(1):3189–3196. https://doi.org/10.1016/j.egypro.2009.02.102
41. Sun W, Wu A, Hou K, Yang Y, Liu L, Wen Y (2016) Real-time observation of meso-fracture process in backfill body during mine subsidence using X-ray CT under uniaxial compressive conditions. Constr Build Mater 113:153–162. https://doi.org/10.1016/j.conbuildmat.2016.03.050
42. Sun W, Hou K, Yang Z, Wen Y (2017) X-ray CT three-dimensional reconstruction and discrete element analysis of the cement paste backfill pore structure under uniaxial compression. Constr Build Mater 138:69–78. https://doi.org/10.1016/j.conbuildmat.2017.01.088
43. Taron J, Elsworth D (2009). Thermal-hydrologic-mechanical-chemical processes in the evolution of engineered geothermal reservoirs. Int J Rock Mecha Min Sci, 46(5):855–864. Retrieved from http://www.sciencedirect.com/science/article/B6V4W-4VPV8XV-1/2/65c7d1ecbd2ba5944cd2ba642208ac1c
44. Teatini P, Gambolati G, Ferronato M, Settari A, Walters D (2011) Land uplift due to subsurface fluid injection. J Geodyn 51(1):1–16. https://doi.org/10.1016/j.jog.2010.06.001
45. Wang J (2010) High-level radioactive waste disposal in China: update 2010. J Rock Mech Geotech Eng 2(1):1–11. https://doi.org/10.3724/SP.J.1235.2010.00001
46. Wang X-S, Jiang X-W, Wan L, Song G, Xia Q (2009) Evaluation of depth-dependent porosity and bulk modulus of a shear using permeability–depth trends. Int J Rock Mech Min Sci 46(7):1175–1181
47. Wang Y, Li CH, Hu YZ (2018) Use of X-ray computed tomography to investigate the effect of rock blocks on meso-structural changes in soil-rock mixture under triaxial deformation. Constr Build Mater 164:386–399. https://doi.org/10.1016/j.conbuildmat.2017.12.173
48. Wang G, Shen J, Liu S, Jiang C, Qin X (2019) Three-dimensional modeling and analysis of macro-pore structure of coal using combined X-ray CT imaging and fractal theory. Int J Rock Mech Min Sci 123:104082. https://doi.org/10.1016/j.ijrmms.2019.104082
49. Wang Y, Feng WK, Wang HJ, Li CH, Hou ZQ (2020) Rock bridge fracturing characteristics in granite induced by freeze-thaw and uniaxial deformation revealed by AE monitoring and post-test CT scanning. Cold Reg Sci Technol 177:103115. https://doi.org/10.1016/j.coldregions.2020.103115
50. Yun TS, Jeong YJ, Kim KY, Min K-B (2013) Evaluation of rock anisotropy using 3D X-ray computed tomography. Eng Geol 163:11–19. https://doi.org/10.1016/j.enggeo.2013.05.017
51. Zabler S, Rack A, Manke I, Thermann K, Tiedemann J, Harthill N, Riesemeier H (2008) High-resolution tomography of cracks, voids and micro-structure in greywacke and limestone. J Struct Geol 30(7):876–887. https://doi.org/10.1016/j.jsg.2008.03.002
52. Zhang, JJ, Bentley LR (2005) Factors determining Poisson’s ratio. CREWES Research Report 17
53. Zhang Y, Zhang Z, Arif M, Lebedev M, Busch A, Sarmadivaleh M, Iglauer S (2020) Carbonate rock mechanical response to CO2 flooding evaluated by a combined X-ray computed tomography – DEM method. Journal of Natural Gas Science and Engineering 84:103675. https://doi.org/10.1016/j.jngse.2020.103675
54. Zheng Z (1993) Compressibility of porous rocks under different stress conditions. Int J Rock Mech Min Sci Geomech Abstr 30(7):1181–1184. https://doi.org/10.1016/0148-9062(93)90091-Q
55. Zhu Q, Zhou Q, Li X (2016) Numerical simulation of displacement characteristics of CO2 injected in pore-scale porous media. J Rock Mech Geotech Eng 8(1):87–92. https://doi.org/10.1016/j.jrmge.2015.08.004
56. Zimmerman, R. W. (1991). Compressibility of sandstones. Amsterdam ; New York : New York, NY, USA: Elsevier ; Distributors for the United States and Canada, Elsevier Science Pub. Co

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-73f1668c-1e0a-488c-8f67-8c5c35115287